{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Load-Data\" data-toc-modified-id=\"Load-Data-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Load Data</a></span></li><li><span><a href=\"#Plot-Data\" data-toc-modified-id=\"Plot-Data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Plot Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Line-Plot\" data-toc-modified-id=\"Line-Plot-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Line Plot</a></span></li><li><span><a href=\"#Resampling\" data-toc-modified-id=\"Resampling-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Resampling</a></span></li><li><span><a href=\"#Lag-plot\" data-toc-modified-id=\"Lag-plot-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Lag plot</a></span></li><li><span><a href=\"#Autocorrelation-plot\" data-toc-modified-id=\"Autocorrelation-plot-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Autocorrelation plot</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 时间序列变量由特定于某个时间点的值填充。时间是线性的，并且是无限细粒度的，所以时间序列值实际上是区间变量的一种特殊情况。\n",
    "\n",
    "* 原文link： https://www.kaggle.com/residentmario/time-series-plotting-optional/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T12:18:15.991773Z",
     "start_time": "2019-05-07T12:18:15.982711Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "* 股票数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T12:08:24.129336Z",
     "start_time": "2019-05-07T12:08:22.769407Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>626.951088</td>\n",
       "      <td>626.751061</td>\n",
       "      <td>624.241073</td>\n",
       "      <td>629.511067</td>\n",
       "      <td>3927000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>627.181073</td>\n",
       "      <td>623.991055</td>\n",
       "      <td>621.541045</td>\n",
       "      <td>627.841071</td>\n",
       "      <td>6031900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>625.861078</td>\n",
       "      <td>608.261023</td>\n",
       "      <td>606.361042</td>\n",
       "      <td>625.861078</td>\n",
       "      <td>7987100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>609.401025</td>\n",
       "      <td>594.101005</td>\n",
       "      <td>592.651008</td>\n",
       "      <td>610.001045</td>\n",
       "      <td>12876600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>592.000997</td>\n",
       "      <td>602.021036</td>\n",
       "      <td>589.110988</td>\n",
       "      <td>603.251034</td>\n",
       "      <td>9483900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           symbol        open       close         low        high      volume\n",
       "date                                                                         \n",
       "2010-01-04   GOOG  626.951088  626.751061  624.241073  629.511067   3927000.0\n",
       "2010-01-05   GOOG  627.181073  623.991055  621.541045  627.841071   6031900.0\n",
       "2010-01-06   GOOG  625.861078  608.261023  606.361042  625.861078   7987100.0\n",
       "2010-01-07   GOOG  609.401025  594.101005  592.651008  610.001045  12876600.0\n",
       "2010-01-08   GOOG  592.000997  602.021036  589.110988  603.251034   9483900.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks = pd.read_csv(\"nyse/prices.csv\", parse_dates=['date']) # 解析索引\n",
    "stocks = stocks[stocks['symbol'] == \"GOOG\"].set_index('date') # Set the DataFrame index using existing columns.\n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T12:19:01.880203Z",
     "start_time": "2019-05-07T12:19:01.875325Z"
    }
   },
   "source": [
    "* animal shelter outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T12:19:31.191165Z",
     "start_time": "2019-05-07T12:19:30.834843Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_upon_outcome</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>animal_type</th>\n",
       "      <th>breed</th>\n",
       "      <th>color</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthyear</th>\n",
       "      <th>name</th>\n",
       "      <th>outcome_subtype</th>\n",
       "      <th>outcome_type</th>\n",
       "      <th>sex_upon_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2 weeks</td>\n",
       "      <td>A684346</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Orange Tabby</td>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>2014-07-22 16:04:00</td>\n",
       "      <td>2014-07-22T16:04:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Intact Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 year</td>\n",
       "      <td>A666430</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Beagle Mix</td>\n",
       "      <td>White/Brown</td>\n",
       "      <td>2012-11-06</td>\n",
       "      <td>2013-11-07 11:47:00</td>\n",
       "      <td>2013-11-07T11:47:00</td>\n",
       "      <td>Lucy</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Spayed Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 year</td>\n",
       "      <td>A675708</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Pit Bull</td>\n",
       "      <td>Blue/White</td>\n",
       "      <td>2013-03-31</td>\n",
       "      <td>2014-06-03 14:20:00</td>\n",
       "      <td>2014-06-03T14:20:00</td>\n",
       "      <td>*Johnny</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Neutered Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9 years</td>\n",
       "      <td>A680386</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Miniature Schnauzer Mix</td>\n",
       "      <td>White</td>\n",
       "      <td>2005-06-02</td>\n",
       "      <td>2014-06-15 15:50:00</td>\n",
       "      <td>2014-06-15T15:50:00</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Neutered Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5 months</td>\n",
       "      <td>A683115</td>\n",
       "      <td>Other</td>\n",
       "      <td>Bat Mix</td>\n",
       "      <td>Brown</td>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>2014-07-07 14:04:00</td>\n",
       "      <td>2014-07-07T14:04:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rabies Risk</td>\n",
       "      <td>Euthanasia</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_upon_outcome animal_id animal_type                    breed  \\\n",
       "0          2 weeks   A684346         Cat   Domestic Shorthair Mix   \n",
       "1           1 year   A666430         Dog               Beagle Mix   \n",
       "2           1 year   A675708         Dog                 Pit Bull   \n",
       "3          9 years   A680386         Dog  Miniature Schnauzer Mix   \n",
       "4         5 months   A683115       Other                  Bat Mix   \n",
       "\n",
       "          color date_of_birth            datetime            monthyear  \\\n",
       "0  Orange Tabby    2014-07-07 2014-07-22 16:04:00  2014-07-22T16:04:00   \n",
       "1   White/Brown    2012-11-06 2013-11-07 11:47:00  2013-11-07T11:47:00   \n",
       "2    Blue/White    2013-03-31 2014-06-03 14:20:00  2014-06-03T14:20:00   \n",
       "3         White    2005-06-02 2014-06-15 15:50:00  2014-06-15T15:50:00   \n",
       "4         Brown    2014-01-07 2014-07-07 14:04:00  2014-07-07T14:04:00   \n",
       "\n",
       "      name outcome_subtype outcome_type sex_upon_outcome  \n",
       "0      NaN         Partner     Transfer      Intact Male  \n",
       "1     Lucy         Partner     Transfer    Spayed Female  \n",
       "2  *Johnny             NaN     Adoption    Neutered Male  \n",
       "3   Monday         Partner     Transfer    Neutered Male  \n",
       "4      NaN     Rabies Risk   Euthanasia          Unknown  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shelter_outcomes = pd.read_csv(\n",
    "    \"austin-animal-center-shelter-outcomes-and/aac_shelter_outcomes.csv\", \n",
    "    parse_dates=['date_of_birth', 'datetime']\n",
    ")\n",
    "shelter_outcomes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T12:20:34.725332Z",
     "start_time": "2019-05-07T12:20:34.528216Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1156bec88>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shelter_outcomes['date_of_birth'].value_counts().sort_values().plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 2015 年 动物出生的较多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling \n",
    "* 目前的数据是时间刻度是 day，我们修改为 year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T12:22:50.357911Z",
     "start_time": "2019-05-07T12:22:50.063820Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114b43390>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shelter_outcomes['date_of_birth'].value_counts().resample('Y').sum().plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 注意到 pandas 正在自动调整x轴上的标签以匹配我们的输出类型。\n",
    "这是因为Pandas具有“时间意识”;\n",
    "当我们有相隔一年的数据点时，我们只想看到标签上的年份，而不是其他!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lag plot\n",
    "\n",
    "* 数据点和数据集中的前一个点进行比较\n",
    "\n",
    "* 参考： https://www.statisticshowto.datasciencecentral.com/lag-plot/\n",
    "\n",
    "Lag plots allow you to check for:\n",
    "\n",
    "* Model suitability.\n",
    "* Outliers (data points with extremely high or low values).\n",
    "* Randomness (data without a pattern).\n",
    "* Serial correlation (where error terms in a time series transfer from one period to another).\n",
    "* Seasonality (periodic fluctuations in time series data that happens at regular periods)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T12:41:03.897803Z",
     "start_time": "2019-05-07T12:41:03.691034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115508128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import lag_plot\n",
    "\n",
    "lag_plot(stocks['open'].tail(50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 790 附近就存在一个异常的点\n",
    "\n",
    "* 数据近似线性，说明不是随机的\n",
    "\n",
    "* 数据显示线性模式，则表明存在自相关。正线性趋势(即从左到右向上)表明正自相关;负线性趋势(从左到右向下)暗示负自相关。数据在对角线上聚集得越紧密，自相关性越强;完全自相关的数据将聚集在一条对角线上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T12:43:37.149951Z",
     "start_time": "2019-05-07T12:43:36.985092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115a99ba8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lag_plot(stocks['volume'].head(500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autocorrelation plot \n",
    "\n",
    "* 自相关系数, 就是用来表达一组数据前后数据 (自己和自己) 的相关性的\n",
    "默认是 和后一位\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](http://pqvlt7eed.bkt.clouddn.com/f7m37.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-07T13:19:35.166102Z",
     "start_time": "2019-05-07T13:19:34.917064Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115b61a90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m6oQJE1ojZMZVKc//bF7N+hmD+jPh/L71ymUBFy9ezNjzzifzwfe99q05kUhFsQIlDE5P5JOcAhbnRzN5cCqwBYBbPyip95o9E2PJysqi87L5dOqazIQJZ/DWmj307hrHGeldeOitDcxevoPVP7+QxLgopv7lY8BJKANPP5MRbg+2xYsX01rnyx8Wl38sLv9YXF+dL1Ved+DcJf9D97HJ3dZSewDP/k1p7jYvIjIJeAC4QlU9uy3vcX/mAouBMwMQU8CEhQk/vSiTNPcekrH96o8L5ik6Ipy8WZeS2a32Tvi31uzl3XX7iIsK57lbnGFhNu49xmPztzT5WtVtIL27xvFZ3mEefHM9P3p5Dfe95jTWv7t+H+DMRrmzoIS1u2u7IxdZlZcx5itqNqGoaqmqPqqqV7uPxzy/2FtgJTBARPq4vcimUad7soicCTyJk0zyPbYniEi0u5wEnIuT6NqUO7P68/F9F7D5kSkMOc23IVD++I0z6NbJu4oqpVMMkeFh/O3Gs2q23XZeHx65cmjN+oTM2tGS77qgPwCllVXsKCjh358687hs3u8Mtx8T6XQ7XrLlIM98sh2A7453rp4am5XSGGOa02iVl4jMUdXrRGQ9zthdXlrahqKqFSJyF/ABTrfhZ1R1o4g8DKxS1bnA/wLxwH9EBGq7Bw8CnhSRKpykOKtO77A2pfoL3BfD0jqz4meTuPqvn9Q06P/8skEAXDysOykdo8k/Xsrkwd0Y07cr3zq7F0eKy4iODOPvS3K5Y3y/mvtU7p44gFueW+n1+mUVVTXx/Gn+1prtt53XhyeX5PLPj7dz8dDu1nXYGOO3ptpQfuT+vCxYB1fVecC8Ott+4bE8qZHnLQOGBSuutuDx689k3vp9fOf8vrjJFIBfXTGE11bv5gyP+1US4pzG/nsuzPB6jayBKTXL3TpFc+BYKRkPvkfHmPq/9pSOzmRiX+ws5Oq/fsI1I9JILGu4w0ZllaKq9bo3G2Pat0a/EVR1n7v4fVXd4fkAvt864bVfaQmxzBjXzyuZgHOV8vT0UT5f9cy8eCA3n9Obv9xQW112/KR3O8n33S7GfZPjAFi7+yi/eGsjT66rrdn0vLly+jOfMea3C/x7Q8aYrz1f/sW8sIFtFwc6EBMc3x3fj19eMYTEOO8uy7ee24e7Jw0gb9al3DtlIAAvzzjbq0zuUeceluXbCsh48D0+d2/M/DjnEAXFZRwtsfYWY0ytRhOKiNzhtp9kisg6j8d2YF1jzzNtU48uHcjoFk9SfDSjeifw04syuXuSdxVZSscY0hM71KyXVjpXJku3OiMXf7T1oNcMk2c8/N/WCd4Yc0poqg3lReA94HfA/R7bj6vq4YafYtqqmMhw/vvj5gd+fP2Oczl4vJSdh4v53r9Xs2rH4ZokEibCYR9mmGzItoNF9O4aZ439xnyNNZpQVPUocBS4HkBEUoAYIF5E4lV1Z+uEaFpTcsdokjtGk+J2Xb7hHyvIcrskV1RpzXwu1VS1XjtPXbsOlzDx/y0hPbEDH917QXACN8aEXLNtKCJyuYhsBbYDS3AGiXwvyHGZEEuKr70XpnrK48cXbK2ZvvjyM04D4NjJ5m+ErJ7FctfhE4EO0xjThvjSKP9r4Gxgi6r2ASbiDNRovuZmjo6pt+2TnEMADOvhjKxcPYVxTn5RzWCWdW3NP16z3FgZY8ypz5eEUq6qBUCYiISp6iKccbXM11xmYjiPTB0CwPgMp9rruWV5AFzg3uPyxKIcFnx5gEmPLuHhtzc2+DpbDxTVLJeU2dAuxnxd+TI4ZKGIxANLgRdEJB/wfXJzc0qbNronlVXKJcO6M9q99yQyXOiX7Ix6+taavby1Zi8Ar6zaxdQze3BWzwRUlYNFpaR0jGFrfm1CKS6tpGOMzbdizNeRLwllKnAS+DHOSMOdgYeDGZRpOyLDw7j53D4AbPzVRUSEOw3wIkLvrrHkFZQQFR5GWWUVJ8uruPqvy4iLCqd/Sjxrdx/lg7vHkZNfRHLHaA4eL+XAsZOkdq5flWaMOfX5MjhksapWqmqFqs5W1cfdKjDTzsRFRxAdEU50hHOX/ivfHQvUznlfrbissmYE44Wb8ykqreCmMb0Qce5lMcZ8PTV1Y+NxETnm8Tju+bM1gzRtU7JHT7CIRu4v+f37mwE4u28i3TrGsHBzfoPl6vrr4hweemtDy4M0xrSapsby6qiqnTweHT1/tmaQpm0KCxNudavDYiLD2fzIFACuG5nGd87v41V2QLeO9EjowOqdhfXmXCmrqOLBN9ez67AzWZiq8of3s5m9fIf1CjPmFOJLGwoich4wQFWfdecf6aiq24MbWuCVlJTw3HPPeW0bMmQIo0aNory8nBdeeKHec4YPH87w4cMpKSmhoemDR44cydChQzl69ChvvPFGvf1jx44lMzOTQ4cO8c4779TbP27cOAD279/P+++/X2//xIkTSU9PZ9euXSxYUH9AxilTppCamkpubi5Lly6tt/+yyy4jKSmJ7Oxsli9fXm//VVddRefOndmwYQOrVnlPxFlYWMjo0aOJjY1lzZo1rFmzpt7zfzrtevIKijkvuYyXX/gXvxwIFB2EIkiN7sL+UqcBfvPaVfQs28HndOI3T80hNqKK1NgwvjP9BhZl5/PvT3fyxcYtfKPHcfafDAcSASgodhr258+fz+7du2viysvLo1OnTlx99dUAvP/+++zfv98rtq5du3L55ZcD8Pbbb1NQ4F1Tm5qaypQpThJ8/fXXOXbM+8I7LS2NSZOcAa/nzJlDSYn37Jh9+vRh/Hhn9IEXXniBgwcPkpeXV7M/IyODc845B6De5w5a77PX0OcenM9e3759Q/bZS01NBWjwswdw3XXXNfnZu/HGG4mMjGTlypVs3Fi/h+HNN98MwLJly9iyxXtSusjISG688UYAlixZwvbttV9nhYWF5Ofnc9111wF4ffaqheKzV/25h/qfvfJy73H1gv3Za4wvNzY+BNwHzHQ3RVE7HbBp5yLCw3jm5lEMS4mqt29a2jFuHNOTN+88F4DkaGewyZf2dOafOxJ4c48zbljeIafTYEy4crA0nL/nJda8Rv6xQMzlZoxpDaLadJWCiKzBmV53taqe6W5b19IJtkIhPT1dH3nkkVCHUU9hYSFdunRpvmArC3RcJZXCH7YmeW375cCDvLq3IxuONdzz64a0o2TEe48f1l7OV6BYXP6xuOq75ZZbPlfVZu8/9OXGxjJ1so4CiEhcS4Mz7VOHsPr/vLyxtyM7SurflzKsk3MH/ou7m586ubhC+PO2RL48Xv8qyRjTeny5QvkJMABnXpTfAbcCL6rq/wU/vMDKzMzU7OzsUIdRz+LFi5kwYUKow6gnGHEtys6nS4dITpRXcsM/VjRaLvvXU8h80KnXX/nAJEb9Zj4PXDKI74zry6JFi+h/xhjSE2MB+NfyPH7+1saasskdoxt72aBqT7/HQLC4/BPKuEQkMFcoqvpH4FXgNSAT+EWgkomITBGRbBHJEZH7G9gfLSKvuPtXiEhvj30z3e3ZInJRIOIxwZeVmcKZPRM4p18Sf7imttZ05sUD+f01w3jnB+fx7C2jiI4IZ4I7yvFv530JwLOfbKe8soqluys4/w+L+GLnEcoqqth2sHbghvzj3qMhG2NaT5O9vEQkHJivqlnAh4E8sPvaT+Bc+ewGVorIXFXd5FHsNuCIqvYXkWnA74FvishgYBowBDgNmC8iGapaGcgYTXBdNyqdsDDhzS/2cOt5fYisM0f9Dy7oz+Lsg7zxxR4A9h49yYAHage6nvn6ejbvP07vrrE12wptFkljQqbJhKKqlSJSJSKd3flRAmk0kKOquQAi8jLOMC+eCWUq8Et3+VXgL+JMvjEVeFlVS4HtIpLjvl79vokedu3a1SYvZa0REC5soN9gRWQ8jLij0eds3u+MYpxXUNud9+57HyDucNPVmsVdMzk44ArSPv8rEeWBG5bOfo/+sbj8E4q4Srr0pShpsM/lfbkPpQhYLyIf4jEopKr+0P/wvPQAdnms7wbGNFZGVStE5CjQ1d3+aZ3n9mjoICIyA5gBTt/zwsLCFoYdeJWVlRZXA5Ta/2Fi174CEk7J6dc2+ZwjXYdSntt42wzA8YHDASgsjyAigO8v1OerMRaXfywu0PAoTvYdR2nf8UiZ7/90+ZJQXncfpyRVfQp4CpxG+YZukAo1awRs3BOLckhPjOWKWZcCThvJ7Hc/Zvlh5677ah/+eBwXPraUiq59+Xz1FwCNTjd84aNL2JpfxDPPPsvoPokNlvFUWaX88+Ncbjq7F7FRjf/JtIXz1RCLyz/tJa7jJ8t5ffUe1uwqpKC4jL5Jcew6XMKJ8krW7zlK6ckKrhx+GrOuOZ0Of7rep9f0pQ1lsqreGIg3UMceIN1jPc3d1lCZ3SISgTPScYGPzzVfA3dm9fdaT+kYw6jUCMaOyOSmfzpXIjPG9aV/SnxNmZueXsHy3AKW/HQCvbrGUVFZRZVCVITTRlM9KVhRqW/tLXPX7uG38zZTUFzGzIsHBeJtGRMSFZVVFBSX8fJnu3hiUU7NwK59kuL4JOcQvbrG0ikmkkuGdueaEWk+/cPlyZc2lF4iEqWqZU2V/QpWAgNEpA9OMpgG3FCnzFxgOk7byLXAQlVVEZkLvCgij+I0yg8APgtwfKYNO7d/V+65MIOJg1IYcppzr8rC/xnPBf9vCctznWEuxv/vYl68fQyPL9zKp7mHee9H59M1Pqpm2uLjPkxfDHDUbegvKbU+H+bUU1WlHDtZzorth/nJf9bWfO7HZyTz/Qn9GJbWmdioCCqrtNGrel/5ch/K88AgnC93zzaUR1t0ZOe1LwH+BIQDz6jqb0TkYWCVqs4VkRjgXzh36h8Gpnk04j+Ac09MBXC3qjY7z73dKe+fUy0uVfhVdnKjz+sWXcGB0tr/oS5OOc6ohJM09ze0rKAD/z0Yz5iEEi7u1nh98ql2vkLN4vKPv3GdrBRWF8bw0eFYTlQ6V+eJkRWM6HKS1JgK+sX53iPS1zvlfWlD2eY+woCOPkfgA1WdB8yrs+0XHssngW808tzfAL8JZDzm1CYCt/Y8womqME5UCm/u8x4U2zOZALyX35Hsomi+3bPpDowV6mSc8Jb982ZMUFQp7DwRSfbxKFJjKjhRGUZCZCXv58dzpDyczhGVpMaWkRFfxrBOJ4mPCN4I3s0mFFX9FYA7DTCqWtT0M04tbWG04Z07dzJw4MA2N9owhG7EV4DY2Fi/R3ztGVtbjTWxbxU/+MT5z+yG/lW8mFP/Pt7ckiimT5/OG2+80eiIr/ve+5KFS3Kp+2dYd8TXutrKaMPz5s0jP7/+PDShHm144MCBTJky5Wvz2fPU0tGGu5/Wg0OdM7hgYAofffC210jXqpCS1pt+w87ijdV7eO3TrRwqC6/3/pPio3j+W8PJXfZuvX3BGm242YQiIkNxqp0S3fVDwLdVtf5v0Jg25pqz0iguraBDxd6abTemHeVweRjzD3akvAp2HznR5GsUFjtVA8sPxzIxqZiIZsaXUIVNx6PoZ3O5GFdBWRifHo5lbGIJHSOqGi1XqVBYHs7c9RWsPriB1E4xDOoQSVFpPPtL4ig9EkV+aQRkF8OCjwDoHCkMii9lWOeT7D0ZSW5xJKPTO/Kzb5xLSqcYcpe11rv0rQ1lGfCAqi5y1ycAv1XVc4IfXmDZWF7++TrFtbOghHH/uwiA7b+7BBFhza5CrnziE24c05P4mIiaHlwFRaU8vmAr145Ip2diLGc8/N+a13nxO2M4p19Sg8eojmvu2r388KUvePDSQdx+ft+v9iYD6Ov0ewyGyirliUU5jMtIZnh6l2bj2nW4hMcXbGXD3mOM7p3APZMz6RAZTv7xk4SJIOK85l8W5nC4uIy9R0+wYY/3FcivrhhCXHQESfFRpCV0ILVzB3YfKeGeV9ayaV9t2biocIrLvDuDDEiJJzEuioxuHblhTE8GdQ/+fIe+juXlSxtKXHUyAVDVxTbisDnV9Oway7bfXkJlleIMtgADUzsSJvDCip0ATB6cyjV/q/13btm2Akb29u42+eW+440mlGr7jzpXPL9+90uG9ujM2X27BvKtmAB744s9PPrhFh79cAvVrC2TAAAZMUlEQVQ/vKA/jy8s5s7SzUSEhXFWrwRmL8ujpKyC8DDh8x1HOFlee4Xx5b5jFBSXocC76/Y1eZxbz+3Diu0FbNx7jIfmNlzBIwLnD0ji2hFpTBmaSpgIxaUVFJVWsHXNCrKysgL51gPOl4SSKyI/x6n2ArgJyA1eSMYER3iYeHWLjIkMp09SXM3gkp7JBGBrfhFb850mw9vO68Nba/awed8x/mfOWg4VlTL71tEA7D5SQkRYbT3Yl/uO1yxPe+pT8tybMk3bs6OgmEfeqR3t6fGFOQA8sWhbo8/pnxLPdSPTuPqsNP7xUS5PLvH+OoyJDKN75w48eOkgNu09xg1jetI1vnYE7BNllcxenkfuwSJOlFexbnchOwpKuGBgCvdNGUhmqnffpy6xUXSJjSJH2n6vEF8Syq3Ar3DullfgI3ebMae8s3omeI1WXO331wzjvtfW16z/7JJBbN5/jP98Xts4e6S4jKJSZ+TjhNhIfntOJM8vz6sZzBKcKguAoyfKiQoPo0NU/cZTgIPHS0mMi/LpPoDqamo5Bb5g2qJl2w7xfwtymDykG79620kmL884mxG9EnhqaS77d23nWFQSOw+XUFxaQc/EWBJiozinf1fG9k0itXPtZHA/nZxJSscYjp8s544J/YiO8P79ThzUrd7xO0SF873x/YL7JkPEl15eR4CWjttlTJv08NShjOiVQGZqRw4XlzGweyfKK6pIT4ylX3I8D7+ziXEDkgkPk3qjIZ/5SO0A3EdKyrljfjngVGUMTO3IlKGp/HnBVo4Ul3HmIx9yRnoX3rrzXJZuOci2g0VMGtSN6MgwZi/L44lF2/j22F48PHVoszFf9Kel9EmK48lvNVul3S499NYGKlXZUVBCcsdoJg9OZcrQVNbtLmTtrsKauXOqb4D9122ja6ol78zqz+LFu5kw4UyfjhURHsZt5/UJzhs5BfnSy+tD4BuqWuiuJ+CM9GtzkJhTXoeocKaN7tngvpG9E5l713k16w9eOojF2QfpmRjLzsO13TjrNpyGCTx/22g+2nII1drEs3ZXIY8v2MqjHzpdWF9YsZPz+ifx3LI8AJ5fvqPZhHL8ZDlbDhSx5UARxaUVxEU3X8nw2tYybn7/XZ69ZRRZmSnNlj+VnSyvZPbyHV7bXl9df1Sm0zrHcPnw07h+VE96J1mTcKD4MgVwUnUygZorlq/3p9KYBvRP6UjerEtZem8WMZHOn85vrxrGspkTOc2jGuTnlw0mpWMMFw1Nrfca1ckEICe/iHfW7fXa31Cvy4rKKh79bzbbDxWzIvdwzfYhD33AFzuPNBv329ucbs+3PLuywdc/FeTkH2eXRxJvSEFRKQN/Xns/Tf+UeK/fC0Bmt448fv2ZLJs5kZkXD7JkEmC+tKFUiUhPVd0JICK9oN49Xsa0K4t/kkXhiTIGpjpdNj+8ZzwvzlvCxPPG0Kur8yUVHx3B098eyTvr9vLYN4fzwoqdPPjmBq/XOVTkPUTer97exPjMZKIjwmp6k/3jo+08vjCHxxfmkNEtntiocMorqyivVK7667J6jf7llVWox2CYnv716Q6uH92zXvVda3pn3V4GpPg+6EZRaQWTHl1KmEDu77zfa0VlFat3FvLKyl28trq2fWv9LycTHx2BiPDFziPM//IAd2UNaLQNywSGLwnlAeBjEVkCCHA+7vwixrRXqZ1jvBpn46IjGJAQTt/keK9ykwZ3Y9Jgp2H2prN78UnOIc5I70L3zjH86GXn7u95PzyfhLhIxv5uIc8ty6upAkuMi+InkzN5YlFOzettOVDEtSPS+OM3zuDcWQvZU3iCYyfL6RQTSVlFFWt2FXLdk85d6W/deS6DT+tEuEBq5w7sKTzBL97ayAuf7uSDH49r9L3tKTxBmED3zh2aPQ+VVUp5ZRUxkb59UZdWVHLXi870AjNHxzBetdnOBUMf+gBwhhhZu6uQM9Kd8axW5h3mxRU7vTpBZHSL58FLB9MxJrJm25k9EzizZ4JP8ZmW8aVR/n0ROQs42910t6oeCm5Yxnw9/e2mEYDTdfTeKZncOKYXnTs4X35PfmsE3/3X5zVlDxeX8bM3nJ5mv71qWM3yrKuHAfDnacO59u/LeX/DfvqnxPPDl77wuuv/pn+uYNyAZCoVfn3lUJZsOchzy/LIPnCcvYUnOK1Lwwljyp+WEhsVzoqfTWryvagqD7yxnrfX7uUHEwdwTr+unJ7W9OCFc1bWzqn3u89O8rvP5vHid8Zw7EQF2fuPM6BbPKUVlcxe5rSDTB1+mtfzpz7xSb3zBDC2b1duObc3k4fUr2Y0rceXKxSAcwDPf2nqD0pljPFZh6hwvj/Be66Xi4ak8uClg/jvxgN8lnfYa991I9M4f0ASVapEuNVVI3olkBgXxb2vrvMq+82R6UwZmsotz63k3fX7SIgWsgamMD4jmX4p8fz8zQ2cM2shg7p34q6s/vRNjiNMhKc/yuWK4adx/GQFx09W0Pv+d/nDNadz3ah0r9fPyS9i0qNLvLbNem8zUD2tQCbREWFs2nuMrvFRJMZF8cwneVw0pFtND6vrRqYxZ5VTRXXDPxqfYXPNLqf59uP7snh33T5+997mmmTSNS6KaaPTmT62NymdYhp9DdN6fOnlNQsYBVSPIPYjETlHVX8W1MiMaYduP78vt5/fl8XZ+bz02U4eunwIIk731PTEWK+yIsL0sb15bL7T0H/vlMyaJKWqXD+6Jy99tpPvneHcVBcWJnzr7F784f3NHD9ZwZf7jnHni6u9XtPzPhuAe19bx3837Wf+l/l0iongoiGp9cp07xzDvqPOpGWf5BTwSU7Dg0e9vdbpgHDHhH7cN2UgU7oeZndMH37hJpk/fuMMPti4nw83HQDg22N7ER8dwajeiaQlxHL7+X2pVCUpLpqJg1K8bhY0bYMvVyiXAMNVtQpARGYDXwCWUIwJkgmZKUzwoYvvDyf259z+XTmrZwJhHjdFigiPTB3C/RcP5IsVn3g95683nsXybQXkFRQzb/3+ui/JtSPSuPmc3qzYfphH3tnE/C+dkYqPnayoSSZ9kuJ48TtjyD1YzDn9urL7yAku/vNHhIcJiXFRbD/kfbNox+gIjpdWcMmwVO6eNACAMBG+PbY3PRNj6dU1jj5JcVw7Ig1VRRWv9wPOSAd1r+pM2+JrlVcXnAmuwJmG1xjTBohIvfHGqkWEh9G5Q/3eXOcPSOb8Ac5EZBv3HmXNrkKG9ejMws35zP/yAD+/dDCdYyMZ2qMz3TpFc6KsksoqZdm2AjbuPcqfp53J0B7O10B1w316Yiyrf35hTc+yTXuPER8dwcq8w6R0iq45XkPqJk5xB1g0px5fEsrvgC9EZBFOL69xwMygRmWMaRVDTutcM4Xy6WlduHtShtf+y06vbRRv7AbQap7dlAef5nSn7tk1trHi5mvIl15eL4nIYpx2FID7VLX+dbIxxph2rdm7m0RkgaruU9W57mO/iNSfvs0PIpIoIh+KyFb3Z71O4iIyXESWi8hGEVknIt/02PeciGwXkTXuY3hL4jHGGNNyjSYUEYkRkUQgSUQS3CSQKCK9gR4tPO79wAJVHQAscNfrKsGZGXIIMAX4k4h4dnL/qaoOdx/15wc1xhjTqpqq8voucDdwGuDZt/AY8JcWHncqMMFdng0sBu7zLKCqWzyW94pIPpAMFGKMMabN8WUK4B+o6v8F9KAiharaxV0W4Ej1eiPlR+MkniGqWiUizwFjgVLcKxxVLW3kuTNwh4pJTk4eMWfOnEC+lYAoKioiPj6++YKtzOLyj8XlH4vLP6GMKysry6cpgN0+340/gG839PDhefOBDQ08pgKFdcoeaeJ1ugPZwNl1tgkQjZNoftFcPKpKRkaGtkWLFi0KdQgNsrj8Y3H5x+LyTyjjAlapD9+xvnQbHuWxHANMxKkCe76ZRNXoQEAickBEuqvqPhHpDuQ3Uq4T8C7wgKp+6vHa1ZM3l4rIs8BPfHgfxhhjgsiXbsM/8Fx3G8ZfbuFx5wLTgVnuz7fqFhCRKOAN4HlVfbXOvupkJMCVOFc+xhhjQuirTIpQDPRt4XFnAReKyFZgkruOiIwUkafdMtfh3ER5cwPdg18QkfXAeiAJ+HUL4zHGGNNCvgwO+Ta1E2qFA4OAFrVsq2oBTtVZ3e2rgNvd5X8D/27k+Re05PjGGGMCz5c2lD96LFfgJJVvNlLWGGNMO+VLG8oSETkTuAH4BrAdeC3YgRljjDm1NJpQRCQDuN59HAJewblvJauVYjPGGHMKaeoKZTPwEXCZquYAiMiPWyUqY4wxp5ymenldDewDFonIP0RkIs7NhMYYY0w9jSYUVX1TVacBA4FFOON6pYjI30RkcmsFaIwx5tTQ7H0oqlqsqi+q6uVAGs70v/c18zRjjDHtjF83NqrqEVV9SlXr3UNijDGmffsqd8obY4wx9VhCMcYYExCWUIwxxgSEJRRjjDEBYQnFGGNMQFhCMcYYExCWUIwxxgSEJRRjjDEBYQnFGGNMQFhCMcYYExAhSSgikigiH4rIVvdnQiPlKj3mk5/rsb2PiKwQkRwReUVEolovemOMMQ0J1RXK/cACVR0ALHDXG3JCVYe7jys8tv8eeExV+wNHgNuCG64xxpjmhCqhTAVmu8uzgSt9faKICHAB8OpXeb4xxpjgEFVt/YOKFKpqF3dZgCPV63XKVQBrgApglqq+KSJJwKfu1Qkikg68p6pDGznWDGAGQHJy8og5c+YE5T21RFFREfHx8aEOox6Lyz8Wl38sLv+EMq6srKzPVXVkswVVNSgPYD6woYHHVKCwTtkjjbxGD/dnXyAP6AckATkeZdKBDb7ElJGRoW3RokWLQh1Cgywu/1hc/rG4/BPKuIBV6sN3bFNzyreIqk5qbJ+IHBCR7qq6T0S6A/mNvMYe92euiCwGzgReA7qISISqVuBM+rUn4G/AGGOMX0LVhjIXmO4uTwfeqltARBJEJNpdTgLOBTa52XIRcG1TzzfGGNO6QpVQZgEXishWYJK7joiMFJGn3TKDgFUishYngcxS1U3uvvuAe0QkB+gK/LNVozfGGFNP0Kq8mqKqBUC9aYRVdRVwu7u8DBjWyPNzgdHBjNEYY4x/7E55Y4wxAWEJxRhjTEBYQjHGGBMQllCMMcYEhCUUY4wxAWEJxRhjTEBYQjHGGBMQllCMMcYEhCUUY4wxAWEJxRhjTEBYQjHGGBMQllCMMcYEhCUUY4wxAWEJxRhjTEBYQjHGGBMQllCMMcYEhCUUY4wxAWEJxRhjTECEJKGISKKIfCgiW92fCQ2UyRKRNR6PkyJypbvvORHZ7rFveOu/C2OMMZ5CdYVyP7BAVQcAC9x1L6q6SFWHq+pw4AKgBPivR5GfVu9X1TWtErUxxphGhSqhTAVmu8uzgSubKX8t8J6qlgQ1KmOMMV9ZqBJKN1Xd5y7vB7o1U34a8FKdbb8RkXUi8piIRAc8QmOMMX4RVQ3OC4vMB1Ib2PUAMFtVu3iUPaKq9dpR3H3dgXXAaapa7rFtPxAFPAVsU9WHG3n+DGAGQHJy8og5c+Z89TcVJEVFRcTHx4c6jHosLv9YXP6xuPwTyriysrI+V9WRzRZU1VZ/ANlAd3e5O5DdRNkfAU81sX8C8I4vx83IyNC2aNGiRaEOoUEWl38sLv9YXP4JZVzAKvXhOzZUVV5zgenu8nTgrSbKXk+d6i73CgUREZz2lw1BiNEYY4wfQpVQZgEXishWYJK7joiMFJGnqwuJSG8gHVhS5/kviMh6YD2QBPy6FWI2xhjThIhQHFRVC4CJDWxfBdzusZ4H9Gig3AXBjM8YY4z/7E55Y4wxAWEJxRhjTEBYQjHGGBMQllCMMcYEhCUUY4wxAWEJxRhjTEBYQjHGGBMQllCMMcYEhCUUY4wxAWEJxRhjTEBYQjHGGBMQllCMMcYEhCUUY4wxAWEJxRhjTEBYQjHGGBMQllCMMcYEhCUUY4wxAWEJxRhjTEBYQjHGGBMQIUkoIvINEdkoIlUiMrKJclNEJFtEckTkfo/tfURkhbv9FRGJap3IjTHGNCZUVygbgKuBpY0VEJFw4AngYmAwcL2IDHZ3/x54TFX7A0eA24IbrjHGmOaEJKGo6peqmt1MsdFAjqrmqmoZ8DIwVUQEuAB41S03G7gyeNEaY4zxRUSoA2hCD2CXx/puYAzQFShU1QqP7T0aexERmQHMcFdLRWRDEGJtqSTgUKiDaIDF5R+Lyz8Wl39CGVcvXwoFLaGIyHwgtYFdD6jqW8E6bl2q+hTwlBvTKlVttM0mVCwu/1hc/rG4/GNxfXVBSyiqOqmFL7EHSPdYT3O3FQBdRCTCvUqp3m6MMSaE2nK34ZXAALdHVxQwDZirqgosAq51y00HWu2KxxhjTMNC1W34KhHZDYwF3hWRD9ztp4nIPAD36uMu4APgS2COqm50X+I+4B4RycFpU/mnj4d+KoBvI5AsLv9YXP6xuPxjcX1F4vzDb4wxxrRMW67yMsYYcwqxhGKMMSYg2kVCaWwIl1Y6drqILBKRTe5wMz9ytyeKyIcistX9meBuFxF53I11nYicFeT4wkXkCxF5x11vcFgbEYl213Pc/b2DGFMXEXlVRDaLyJciMrYtnC8R+bH7O9wgIi+JSEwozpeIPCMi+Z73VH2V8yMi093yW0VkepDi+l/397hORN4QkS4e+2a6cWWLyEUe2wP699pQXB77/kdEVESS3PWQni93+w/cc7ZRRP7gsb1VzleLqOrX+gGEA9uAvkAUsBYY3IrH7w6c5S53BLbgDCXzB+B+d/v9wO/d5UuA9wABzgZWBDm+e4AXgXfc9TnANHf578Ad7vL3gb+7y9OAV4IY02zgdnc5CugS6vOFc/PsdqCDx3m6ORTnCxgHnAVs8Njm1/kBEoFc92eCu5wQhLgmAxHu8u894hrs/i1GA33cv9HwYPy9NhSXuz0dp9PPDiCpjZyvLGA+EO2up7T2+WrRewrVgVvtDTo9yT7wWJ8JzAxhPG8BFwLZQHd3W3cg211+Erjeo3xNuSDEkgYswBnK5h33j+iQxxdAzblz//DGussRbjkJQkydcb64pc72kJ4vakduSHTf/zvARaE6X0DvOl9Efp0f4HrgSY/tXuUCFVedfVcBL7jLXn+H1ecrWH+vDcWFM3zTGUAetQklpOcL5x+USQ2Ua9Xz9VUf7aHKq6EhXBodqiWY3GqPM4EVQDdV3efu2g90c5dbM94/AfcCVe56U8Pa1MTl7j/qlg+0PsBB4Fm3Ku5pEYkjxOdLVfcAfwR2Avtw3v/nhP58VfP3/ITi7+JWnP/+Qx6XiEwF9qjq2jq7Qn2+MoDz3WrSJSIyqo3E5ZP2kFDaBBGJB14D7lbVY5771PnXolX7b4vIZUC+qn7emsf1QQRONcDfVPVMoBinCqdGiM5XAjAVJ+GdBsQBU1ozBl+F4vw0R0QeACqAF9pALLHAz4BfhDqWBkTgXAWfDfwUmCMiEtqQfNceEkpjQ7i0GhGJxEkmL6jq6+7mAyLS3d3fHch3t7dWvOcCV4hIHs5IzhcAf8Yd1qaBY9fE5e7vjDMMTqDtBnar6gp3/VWcBBPq8zUJ2K6qB1W1HHgd5xyG+nxV8/f8tNrfhYjcDFwG3Ogmu1DH1Q/nH4O17uc/DVgtIqkhjgucz//r6vgMp/YgqQ3E5ZP2kFAaHMKltQ7u/nfxT+BLVX3UY9dcnGFjwHv4mLnAt93eJmcDRz2qMgJGVWeqapqq9sY5JwtV9UYaH9bGM95r3fIB/y9YVfcDu0Qk0900EdhEiM8XTlXX2SIS6/5Oq+MK6fny4O/5+QCYLCIJ7tXXZHdbQInIFJxq1StUtaROvNPE6Q3XBxgAfEYr/L2q6npVTVHV3u7nfzdOx5n9hPh8AW/iNMwjIhk4De2HCOH58kuoGm9a84HTc2MLTm+IB1r52OfhVD+sA9a4j0tw6tMXAFtxenUkuuUFZ2KxbcB6YGQrxDiB2l5efXE+qDnAf6jtbRLjrue4+/sGMZ7hwCr3nL2J06sm5OcL+BWwGWeCuH/h9Lhp9fMFvITTjlOO82V421c5PzhtGjnu45YgxZWDU8df/dn/u0f5B9y4soGLPbYH9O+1objq7M+jtlE+1OcrCvi3+xlbDVzQ2uerJQ8besUYY0xAtIcqL2OMMa3AEooxxpiAsIRijDEmICyhGGOMCQhLKMYYYwLCEooxrUBEikIdgzHBZgnFGGNMQFhCMSZERORydxDAL0Rkvoh0c7cnizOnyUZ3cMwd1fN1GNOWWUIxJnQ+Bs5WZxDMl3GGKAF4CGeoliE4Y5n1DFF8xvglovkixpggSQNecQdzjMKZBwac4XquAlDV90XkSIjiM8YvdoViTOj8H/AXVR0GfBdn/C9jTlmWUIwJnc7UDjXuOUf5J8B1ACIyGWdwTGPaPBsc0phWICJVwF6PTY/ijA77GHAEWAiMUtUJIpKCMxJtN2A5zlwivVW1tHWjNsY/llCMaWNEJBqoVNUKERmLM3vl8FDHZUxzrFHemLanJ87Ur2FAGfCdEMdjjE/sCsUYY0xAWKO8McaYgLCEYowxJiAsoRhjjAkISyjGGGMCwhKKMcaYgPj/QrR1mp8vHdQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import autocorrelation_plot\n",
    "\n",
    "autocorrelation_plot(stocks['volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "oldHeight": 122,
   "position": {
    "height": "40px",
    "left": "450px",
    "right": "20px",
    "top": "119px",
    "width": "250px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "varInspector_section_display": "none",
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 "nbformat": 4,
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}
